Jun Wang

The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

This book describes systematically the theory and technology of the precision forming of large, complex and thin-walled superalloy castings for aircraft engines, covering all the important basic aspects of the manufacturing process, including process design, wax pattern, ceramic molds, casting and solidification, heat treatment, repair casting and dimension precision control. The correlation of casting defects, structural characteristics and performance of castings is revealed through a range of tests. It also discusses the latest technologies and advances in this field – such as imaging the solidification process by means of synchrotron radiography, 3D computerized tomography and reconstruction of microporosity defects, analysis and diagnosis of error sources for dimension over-tolerance and adjusted pressure casting technology – which are of particular interest. Providing essential insights, the book offers a valuable guide to the design and manufacture of superalloy casting parts for aircraft engines.

The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

This book describes systematically the theory and technology of the precision forming of large, complex and thin-walled superalloy castings for aircraft engines, covering all the important basic aspects of the manufacturing process, including process design, wax pattern, ceramic molds, casting and solidification, heat treatment, repair casting and dimension precision control. The correlation of casting defects, structural characteristics and performance of castings is revealed through a range of tests. It also discusses the latest technologies and advances in this field – such as imaging the solidification process by means of synchrotron radiography, 3D computerized tomography and reconstruction of microporosity defects, analysis and diagnosis of error sources for dimension over-tolerance and adjusted pressure casting technology – which are of particular interest. Providing essential insights, the book offers a valuable guide to the design and manufacture of superalloy casting parts for aircraft engines.

Online advertising is now one of the fastest advancing areas in the IT industry. In display and mobile advertising, the most significant technical development in recent years is the growth of Real-Time Bidding (RTB), which facilitates a real-time auction for a display opportunity. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large number of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimization in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. Despite its rapid growth and huge potential, many aspects of RTB remain unknown to the research community for a variety of reasons. This monograph offers insightful knowledge of real-world systems, to bridge the gaps between industry and academia, and to provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of the new frontier of computational advertising. The topics covered include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimization, statistical arbitrage, dynamic pricing, and ad fraud detection. This is an invaluable text for researchers and practitioners alike. Academic researchers will get a better understanding of the real-time online advertising systems currently deployed in industry. While industry practitioners are introduced to the research challenges, the state of the art algorithms and potential future systems in this field.

Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. In this book we provide a comprehensive and up-to-date introduction toDynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics. The theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren't reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of session search and online advertising. Change is at the heart of modern Information Retrieval systems and this book will help equip the reader with the tools and knowledge needed to understand Dynamic Information Retrieval Modeling.

This book presents an empirical study that develops and validates a learning-oriented self-assessment instrument for assessing the strategic competence (SC) of young language learners in oral communication, specifically within the context of early English education in China. The instrument’s development followed a multi-phased research design, encompassing three interconnected stages: conceptualisation, operationalisation and validation. Each phase employed distinct methods, data collection techniques and analyses tailored to specific research objectives. By adopting an integrative approach, this book clarifies the crucial yet elusive concept of SC. It not only contributes to the field of language assessment but also underscores the importance of explicit SC instruction in language education for young learners. Focusing on real-world classroom scenarios and offering practical solutions for integrating SC instruction into current teaching paradigms, this book will appeal to educators, researchers and policymakers interested in language testing and assessment, foreign language education and applied linguistics.

An up-to-date and accurate discussion of spiking neural P systems in time series analysis In Spiking Neural P Systems for Time Series Analysis, the authors explore the fundamentals and the current states of both spiking neural P systems and time series analysis, examining the application models of time series analysis. You’ll also find walkthroughs of recurrent-like, echo-like, and reservoir computing models for time series prediction. The book covers applications in time series analysis such as financial time series analysis, power load forecasting, photovoltaic power forecasting, and medical signal processing, and contains illustrative photographs and tables designed to improve reader understanding. Readers will also find: A thorough introduction to the theoretical and application research relevant to membrane computing and spiking P neural systems Comprehensive explorations of a variety of recurrent-like models for time series forecasting, including LSTM-SNP and GSNP models Practical discussions of common problems in reservoir computing models, including classification problems Complete evaluations of models used in financial time series analysis, power load forecasting, and other techniques Perfect for scientists, researchers, postgraduates, lecturers, and teachers, Spiking Neural P Systems for Time Series Analysis will also benefit undergraduate students interested in advanced techniques for time series analysis.

This book introduces the ecological, low-carbon, and sustainable concept of ideal cities from different views. With beliefs and practices of scholars in the past in pursuit of ideal cities, it is written with historical events in the development of low-carbon cities. In this way, activities about carbon peaking and carbon neutrality happening worldwide at present can be shown, and efforts variety countries have paid off can be exhibited. Besides, as for the centralized and the distributed space structure in urban evolution, the book discusses their trends and comes up with an ideal model, the distributed city, to realize carbon neutrality based on the distributed structure. Therefore, with a practical example in planning, the feasibility of the distributed city is analyzed in several fields, including space, energy, traffic, water, waste disposal, etc. The book can be used as materials for professionals in city planning and construction, low-carbon development and other fields, as well as a way for ordinary people to learn about the complex city.

This book presents an empirical study that develops and validates a learning-oriented self-assessment instrument for assessing the strategic competence (SC) of young language learners in oral communication, specifically within the context of early English education in China.The instrument’s development followed a multi-phased research design, encompassing three interconnected stages: conceptualisation, operationalisation and validation. Each phase employed distinct methods, data collection techniques and analyses tailored to specific research objectives. By adopting an integrative approach, this book clarifies the crucial yet elusive concept of SC. It not only contributes to the field of language assessment but also underscores the importance of explicit SC instruction in language education for young learners.Focusing on real-world classroom scenarios and offering practical solutions for integrating SC instruction into current teaching paradigms, this book will appeal to educators, researchers and policymakers interested in language testing and assessment, foreign language education and applied linguistics.

Membrane computing is a class of distributed and parallel computing models inspired by living cells. Spiking neural P systems are neural-like membrane computing models, representing an interdisciplinary field between membrane computing and artificial neural networks, and are considered one of the third-generation neural networks. Models and applications constitute two major research topics in spiking neural P systems. The entire book comprises two parts: models and applications. In the model part, several variants of spiking neural P systems and fuzzy spiking neural P systems are introduced. Subsequently, their computational completeness is discussed, encompassing digital generation/accepting devices, function computing devices, and language generation devices. This discussion is advantageous for researchers in the fields of membrane computing, biologically inspired computing, and theoretical computer science, aiding in understanding the distributed computing model of spiking neural P systems. In the application part, the application of spiking neural P systems in time series prediction, image processing, sentiment analysis, and fault diagnosis is examined. This offers a novel method and model for researchers in artificial intelligence, data mining, image processing, natural language processing, and power systems. Simultaneously, it furnishes engineering and technical personnel in these fields with a powerful, efficient, reliable, and user-friendly set of tools and methods.

This book shows a panorama of sustainable development practices covering 70 major cities. This book has created the analysis framework of the “New Normal” of China’s economy, demonstrated the features and connotation of the "New Normal", carried out in-depth analysis and systematic study on the connotation and extension of the “New Normal” of China’s economy from ten aspects including growth shift, structural upgrading, innovation drive, regional synergy, moderate inflation, reform bonus, opening-up forced, risk exposure, sustainable development and macro-control in details and proposed targeted policy suggestions with practical application value that adapt to the new normal of China's economy and ensure the sustained, steady and healthy operation of the macro-economy.

This book shows a panorama of sustainable development practices covering 70 major cities. This book has created the analysis framework of the “New Normal” of China’s economy, demonstrated the features and connotation of the "New Normal", carried out in-depth analysis and systematic study on the connotation and extension of the “New Normal” of China’s economy from ten aspects including growth shift, structural upgrading, innovation drive, regional synergy, moderate inflation, reform bonus, opening-up forced, risk exposure, sustainable development and macro-control in details and proposed targeted policy suggestions with practical application value that adapt to the new normal of China's economy and ensure the sustained, steady and healthy operation of the macro-economy.

This text provides a comprehensive treatment of collaborative filtering and recommender systems, covering both theoretical and practical aspects. It fulfills the urgent need for a textbook that helps readers get quickly acquainted with the field, particularly the newest Web 2.0 and its applications. It provides sample code or software library for algorithms in the book and includes intensive support for course instructors, including chapter problems and exercises and a website with solutions. This text for advanced undergraduates and first-year postgraduates is also an ideal guidebook for practitioners working on information filtering, data mining algorithms, or Web 2.0 applications.